Evidence map›Paper›PMID 38751729›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2024

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis.

Christian Herz, Nicolas Vergnet, Sijie Tian, Abdullah H Aly, Matthew A Jolley, Nathanael Tran, Gabriel Arenas, Andras Lasso, Nadav Schwartz, Kathleen E O'Neill and 2 more

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Christian HerzChildren's Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States.
Nicolas VergnetUniversity of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States.
Sijie TianUniversity of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States.
Abdullah H AlyUniversity of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States.ORCID https://orcid.org/0000-0001-5401-1184
Matthew A JolleyChildren's Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States.
Nathanael TranJefferson Einstein Hospital, Division of Cardiovascular Diseases, Philadelphia, Pennsylvania, United States.
Gabriel ArenasUniversity of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States.ORCID https://orcid.org/0000-0001-5901-0105
Andras LassoQueen's University, School of Computing, Laboratory for Percutaneous Surgery, Kingston, Ontario, Canada.ORCID https://orcid.org/0000-0002-4220-7064
Nadav SchwartzUniversity of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States.
Kathleen E O'NeillUniversity of Pennsylvania, Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States.
Paul A YushkevichUniversity of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States.
Alison M PouchUniversity of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States.

Funding

NRSA Training CoreTL1TR001880 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI MEAGHER, EMMA ANNE · 2016 to 2025
$10.0M
Penn TMC: Organ-Specific ProjectU54HD104392 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI BARNHART, KURT T · 2020 to 2024
$6.1M
Developing a multi-modality, paradigm-shifting approach for in vivo assessment of the human placenta and the impact of maternal nutrition on its development and functionU01HD087180 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI LICHT, DANIEL JACQUES, SCHWARTZ, NADAV · 2015 to 2015
$4.3M
Computer Modeling of the Tricuspid Valve in Hypoplastic Left Heart SyndromeR01HL153166 · NHLBI · CHILDREN'S HOSP OF PHILADELPHIA · PI JOLLEY, MATTHEW · 2020 to 2024
$3.7M
4D Multimodal Image-Based Modeling for Bicuspid Aortic Valve Repair SurgeryR01HL163202 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Alison Marie Pouch · 2022 to 2026
$3.5M
A placenta-based strategy for improved clinical prediction of fetal growth trajectory using automated image analysis of placental morphology and vascularityR01HD109739 · NICHD · VANDERBILT UNIVERSITY · PI Ipek Oguz, Nadav Schwartz · 2022 to 2026
$3.1M
Improving the sensitivity and specificity of MRI-based biomarkers in Alzheimer's diseaseRF1AG069474 · NIA · UNIVERSITY OF PENNSYLVANIA · PI WOLK, DAVID A, YUSHKEVICH, PAUL A. · 2021 to 2021
$2.1M
Ex Vivo Imaging of the Aging Brain to Discover Morphology/Pathology AssociationsRF1AG056014 · NIA · UNIVERSITY OF PENNSYLVANIA · PI YUSHKEVICH, PAUL A. · 2023 to 2023
$2.1M
A Platform for Outcomes Data Sharing and Pre-Operative Image-Guided Mechanistic Assessment for Bicuspid Aortic Valve Repair SurgeryK01HL141643 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI POUCH, ALISON MARIE · 2018 to 2022
$532k
NCATS NIH HHS TL1 TR001880NHLBI NIH HHS K01 HL141643NHLBI NIH HHS R01 HL153166NHLBI NIH HHS R01 HL163202NIA NIH HHS RF1 AG056014NIA NIH HHS RF1 AG069474NICHD NIH HHS R01 HD109739NICHD NIH HHS U01 HD087180NICHD NIH HHS U54 HD104392
6 · The paper itself

Abstract

Purpose: Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging. Approach: A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure's medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated. Results: Three users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated. Conclusions: Synthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.

Indexed as

image analysismedial axisopen source softwareskeletonization

Identifiers

PMID38751729
PMCPMC11092146

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.